Relationship between rock physical properties and spectral mineralogy applied to exploration for an unconformity-related uranium deposit (Saskatchewan, Canada)
Bibliographic record
Abstract
Infrared (IR) spectroscopy has been used to characterize clay and clay-sized minerals present in drill cores that are associated with unconformity-related uranium deposits. Physical properties have been measured on samples to gain empirical data about the rock types and associated relationships with geophysical survey data. These data can be used to build three-dimensional geological models and constrain geophysical inversions. The objective of this study is to verify whether a relationship exists between rock physical properties and IR spectral mineralogy. Physical properties were measured on 427 core samples collected from the Martin Lake project, which is located in the southeastern Athabasca Basin (Saskatchewan, Canada). Results indicate that resistivity, density, and porosity are correlated to each other, especially within basement units. A comparison of their distribution with the IR spectral mineralogy demonstrates a relationship for each altered and unaltered samples. The samples with low resistivity and density, and high porosity are characterized by the presence of a di-trioctahedral (Al–Mg) chlorite (sudoite) due to the hydrothermal alteration processes. The unaltered samples with higher resistivity and density, and low porosity contain a tri-octahedral (Fe–Mg) chlorite as a result of metamorphic processes. Eleven mineralogical classes can be established based on IR spectroscopy. A percentile-based approach has been proposed and tested to define physical property ranges for each of the classes to predict resistivity and density values downhole.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".